RADS Metadata Analysis

Data Collection Methods

Data by Affiliation

Datacite metadata were pulled using the rdatacite package in November 2022. Each of the six institutions were searched using the name of each University in the creators.affiliation.name metadata field. Results were filtered to include DOIs with a publicationYear of 2012 or later, and a resourceTypeGeneral of dataset or software. As the search terms returned other institutions with similar names, results were filtered to include DOIs only from the relevant institutional affiliations.

Following recommendations of the Crossref API, metadata was pulled from the April 2022 Public Release file (http://dx.doi.org/10.13003/83b2gq). DOIs were searched records with a created-dateparts year of 2012 or newer, that had a type of datasets (Crossref does not have software as an available type), and had an author affiliation with one of the six institutions.

Institutional repositories

Upon initial examination of the affiliation data, we realized that our own institutional repositories were not represented in the data because the affiliation metadata field was not completed as part of the DOI generation process.

To pull data shared in our institutional repositories as a comparison, a second search was performed to retrieve DOIs published by the institutional repositories at each university. For the institutional repositories using DataCite to issue DOIs (5 out of the 6 institutions at the time), the datacite API queried by names of the institutional repositories in the publisher metadata field. For the one institution using CrossRef to issue DOIs (Duke), the crossref API was used to retrieve all DOIs published using the Duke member prefixes.

Institutional repository data was then filtered to include only the relevant repositories, datasets and software resource types, and DOIs published in 2012 or later.

Affiliation data from datacite, affiliation data from cross ref, and the institutional repository data were combined into a single dataset.

Analysis

Load required packages and read in combined data.

#packages
pacman::p_load(dplyr, 
               tidyr, 
               ggplot2, 
               rjson,
               rdatacite,
               cowplot, 
               stringr, 
               knitr, 
               DT, 
               ggbreak)



#Load the combined data from 3_Combined_data.R
load(file="data_rdata_files/Combined_ALL_data.Rdata")

#rename object
all_dois <- combined_dois 

#re-factor group so that datacite appears before cross ref
all_dois$group <- factor(all_dois$group, levels = c("Affiliation - Datacite", "Affiliation - CrossRef", "IR_publisher"))

Collapse DOIs by container

Some repositories (such as Harvard’s Dataverse and Qualitative Data Repository) assign DOIs at the level of the file, rather than the study. Similarly, Zenodo often has many related DOIs for multiple figures within a study. In order to attempt to compare study-to-study counts of data sharing, look at the DOIs collapsed by “container”.

by_container <- 
all_dois %>% 
  filter(!is.na(container_identifier)) %>% 
  group_by(container_identifier, publisher, title, institution) %>% 
  summarize(count=n()) %>% 
  arrange(desc(count))

How many publishers have container DOIs?

by_container %>% 
  group_by(publisher) %>% 
  summarize(count=n()) %>% 
  arrange(desc(count)) %>% 
  datatable

Collapsing by container for counts

containerdups <- which(!is.na(all_dois$container_identifier) & duplicated(all_dois$container_identifier))

all_dois_collapsed <- all_dois[-containerdups,]

This leaves a total of 165950 cases.

Overview of the data

DOI types by resource

all_dois_collapsed %>% 
  group_by(resourceTypeGeneral, group) %>% 
  summarize(count=n()) %>% 
  pivot_wider(names_from = group, 
              values_from = count, 
              values_fill = 0) %>% 
  kable()

resourceTypeGeneral	Affiliation - Datacite	Affiliation - CrossRef	IR_publisher
Dataset	11572	147702	2103
Software	4512	0	61

DOI by institutional affiliation/publisher

all_dois_collapsed %>% 
  group_by(group, institution) %>% 
  summarize(count=n()) %>% 
  pivot_wider(names_from = group,
              values_from = count) %>% 
  kable()

institution	Affiliation - Datacite	Affiliation - CrossRef	IR_publisher
Cornell	3921	706	174
Duke	2372	3603	225
Michigan	4188	141111	645
Minnesota	2408	1700	692
Virginia Tech	1553	64	333
Washington U	1642	518	95

Collapse IRs into a single category

Look at all the Institutional Repositories Captured

IR_pubs <- all_dois_collapsed %>% 
  filter(group == "IR_publisher") %>% 
  group_by(publisher_plus) %>% 
  summarize(count = n()) 

IR_pubs %>% 
  kable(col.names = c("Institutional Repository", "Count"))

Institutional Repository	Count
Cornell	174
Duke-Duke Digital Repository	78
Duke-Research Data Repository, Duke University	147
Michigan	10
Michigan-Deep Blue	515
Michigan-ICPSR/ISR	109
Michigan-Other	11
Minnesota	692
Virginia Tech	333
Washington U	95

Replace all of these publishers with “Institutional Repository” so that they will be represented in a single bar.

all_dois_collapsed$publisher[which(all_dois_collapsed$publisher_plus %in% unique(IR_pubs$publisher_plus))] <- "Institutional Repository"

#catch the rest of the "Cornell University Library"
all_dois_collapsed$publisher[which(all_dois_collapsed$publisher == "Cornell University Library")] <- "Institutional Repository"

#and stray VT
all_dois_collapsed$publisher[which(all_dois_collapsed$publisher == "University Libraries, Virginia Tech")] <- "Institutional Repository"

#and DRUM
all_dois_collapsed$publisher[which(all_dois_collapsed$publisher == "Data Repository for the University of Minnesota (DRUM)")] <- "Institutional Repository"

##ICPSR is also inconsistent
all_dois_collapsed$publisher[grep("Consortium for Political", all_dois_collapsed$publisher)] <- "ICPSR"

Overall Count of Data and Software DOIs

Think we just keep these together for the main analysis…

by_publisher_collapse <- all_dois_collapsed %>% 
  group_by(publisher, institution) %>% 
  summarize(count=n()) %>% 
  arrange(institution, desc(count))

Table of publisher counts

by_publisher_collapse_table <- by_publisher_collapse %>% 
  pivot_wider(names_from = institution, 
              values_from = count, 
              values_fill = 0) %>% 
  rowwise %>% 
  mutate(Total = sum(c_across(Cornell:`Washington U`))) %>%
  ungroup() %>% 
   arrange(desc(Total)) %>% 
  mutate(Cumulative_Percent = round(cumsum(Total)/sum(Total)*100, 1))
 

by_publisher_collapse_table %>% 
  datatable

Write out the table of data & software publishers

write.csv(by_publisher_collapse_table, file="data_summary_data/Counts of  Publishers By Insitituion - Collapsed by container.csv", row.names = F)

Graphs

Top 10 publishers of data dois

# by_publisher_dc_collapse <- all_dois_collapsed %>% 
#   group_by(publisher, institution) %>% 
#   summarize(count=n()) %>% 
#   arrange(institution, desc(count))

#table of  publishers - data
by_publisher_dc_collapse_table <- by_publisher_collapse %>% 
  pivot_wider(names_from = institution, 
              values_from = count, 
              values_fill = 0) %>% 
  rowwise %>% 
  mutate(Total = sum(c_across(Cornell:`Washington U`))) %>% 
  arrange(desc(Total))

Look at publishers based on rank of number of DOIs

by_publisher_dc_collapse_table %>% 
  group_by(publisher) %>% 
  summarize(count=sum(Total)) %>% 
  arrange(desc(count)) %>% 
  mutate(pubrank = order(count, decreasing = T)) %>% 
  ggplot(aes(x=pubrank, y=count)) +
  geom_bar(stat="identity") +
  labs(x = "Publisher Rank (Top 20)", y="Number of DOIs")+
  scale_y_break(breaks =c(10000, 100000),scales = .15)  +
  scale_x_continuous(limits = c(0,20), sec.axis = dup_axis(labels=NULL, breaks=NULL)) +
  theme_bw() 

Look at the top 10 publishers - how many does this capture?

top10pubs <- by_publisher_dc_collapse_table$publisher[1:10]

by_publisher_dc_collapse_table %>% 
  group_by(publisher) %>% 
  summarize(count=sum(Total)) %>% 
  mutate(intop10pub = publisher %in% top10pubs) %>% 
  group_by(intop10pub) %>% 
  summarize(totalDOIs = sum(count), nrepos = n()) %>% 
  ungroup() %>% 
  mutate(propDOIs = totalDOIs/sum(totalDOIs)) %>% 
  kable(digits = 2)

intop10pub	totalDOIs	nrepos	propDOIs
FALSE	1428	166	0.01
TRUE	164522	10	0.99

top10colors <- c("Harvard Dataverse" = "dodgerblue2",
                "Zenodo" = "darkorange1",
                "ICPSR" = "darkcyan",
                "Dryad" = "lightgray", 
                "figshare" = "purple", 
                "Institutional Repository" = "lightblue", 
                "ENCODE Data Coordination Center" = "gold2", 
                "Faculty Opinions Ltd" = "darkgreen", 
                "Taylor & Francis" = "red", 
                "Neotoma Paleoecological Database" = "pink")



(by_publisher_plot_collapse <-  by_publisher_collapse %>% 
    filter(publisher %in% top10pubs) %>% 
    ggplot(aes(x=institution, y=count, fill=publisher)) +
    geom_bar(stat="identity", position=position_dodge(preserve = "single")) +
    scale_fill_manual(values = top10colors, name="Publisher")+
    guides(fill = guide_legend(title.position = "top")) +
    #scale_y_continuous(breaks = seq(from = 0, to=5000, by=500)) +
     scale_y_break(breaks =c(3000, 120000),scales = .15)  +
    coord_cartesian(ylim = c(0,5000)) +
    labs(x = "Institution", y="Count of Collapsed DOIs") +
    theme_bw() +
    guides(fill = guide_legend(nrow = 3, title.position = "top")) +
    theme(legend.position = "bottom", legend.title.align = .5))

ggsave(by_publisher_plot_collapse, filename = "figures/Counts of DOIs by Institution_DOIcollapsed.png", device = "png",  width = 8, height = 6, units="in")

Distribution of Top 10 Repos by Institution

by_publisher_percent_plot1 <- by_publisher_collapse %>% 
  group_by(institution) %>% 
  mutate(Percent = count/sum(count)*100) %>% 
  filter(publisher %in% top10pubs) %>% 
  ggplot(aes(x=institution, y=Percent)) +
  geom_col(aes(fill=publisher)) +
  scale_fill_manual(values = top10colors, name="Publisher") +
  labs(x = "Institution", y="Percent of Total Data DOIs") +
  guides(fill = guide_legend(title.position = "top")) +
  theme_bw() +
  theme(legend.position = "bottom", 
        legend.title.align = .5)

publegend <- get_legend(by_publisher_percent_plot1)

by_publisher_percent_plot1 <- by_publisher_percent_plot1 + theme(legend.position = "none")

by_publisher_percent_plot2 <- by_publisher_collapse %>% 
  filter(publisher != "ENCODE Data Coordination Center") %>% 
  group_by(institution) %>% 
  mutate(Percent = count/sum(count)*100) %>% 
  filter(publisher %in% top10pubs) %>%
  ggplot(aes(x=institution, y=Percent)) +
  geom_col(aes(fill=publisher)) +
  scale_fill_manual(values = top10colors, name="Publisher") +
  labs(x = "Institution", y="Percent of Total Data DOIs") +
  theme_bw() +
  theme(legend.position = "none", legend.title.align = .5)

# ggsave(plot = by_publisher_percent_plot1, filename="Percent DOIs Top Publisher Percents - With ENCODE.png", device = "png")
# 
# ggsave(plot = by_publisher_percent_plot2, filename="Percent DOIs Top Publisher Percents - No ENCODE.png", device = "png")


(combined_pub_plots <- plot_grid(plot_grid(by_publisher_percent_plot1,
                    by_publisher_percent_plot2, 
                    labels = c("A", "B")), 
          publegend, 
          nrow=2, 
          rel_heights = c(2,.5), 
          align = "v", 
          axis = "t"))

ggsave(plot = combined_pub_plots, filename="figures/Percent DOIs Top Publisher Percents.png", device = "png", width = 10.5, units = "in")

Overall Proportion of Data/Software DOIs in Top 10 publishers by institution

by_publisher_collapse %>% 
  group_by(institution) %>% 
  mutate(Percent = count/sum(count)*100) %>% 
  filter(publisher %in% top10pubs) %>% 
  group_by(institution) %>% 
  summarize(TotalCount = sum(count), TotalPercent = sum(Percent)) %>% 
  kable(digits =2)

institution	TotalCount	TotalPercent
Cornell	4539	94.54
Duke	5936	95.74
Michigan	145629	99.78
Minnesota	4536	94.50
Virginia Tech	1747	89.59
Washington U	2135	94.68

Institutional Graphs - Collapsed

Cornell

Duke

Michigan

Minnesota

Virginia Tech

Wash U

Repository Poliferation by Year

How many different publishers are researchers sharing their data and how does this change over time?

by_year_nrepos <- all_dois_collapsed %>% 
  group_by(publicationYear, publisher, institution) %>% 
  summarize(nDOIs = n()) %>% 
  group_by(publicationYear, institution) %>% 
  summarize(npublishers = n(), totalDOIs = sum(nDOIs))

by_year_nrepos %>% 
  ggplot(aes(x=publicationYear, y=npublishers, group=institution)) +
  geom_line(aes(color=institution)) +
  labs(x="Year", 
       y="Number of Repositories", 
       title="Number of Repositories Where Data and Software are Shared Across Time") +
  theme_bw() +
  theme(legend.title = element_blank())

Further collapse by Version

We can also look at the data collapsed by version of a record. This was motivated because some repositories have multiple entries for the different versions of the same dataset/collection. And some entries have many versions.

Explore versions

Some Repositories attach “vX” to the doi.

all_dois_collapsed <- all_dois_collapsed %>% 
  mutate(hasversion = grepl("\\.v[[:digit:]]+$", DOI))


all_dois_collapsed %>% 
  filter(hasversion == TRUE) %>% 
  group_by(publisher, hasversion) %>% 
  summarize(count=n()) %>% 
  arrange(desc(count)) %>% 
  datatable()

Some repositories use the “VersionCount”

all_dois_collapsed %>% 
  filter(versionCount > 0) %>% 
  group_by(publisher) %>% 
  summarize(count=n(), AvgNversions = round(mean(versionCount),2)) %>% 
  arrange(desc(count)) %>% 
  datatable()

Some use “metadataVersion”

all_dois_collapsed %>% 
  filter(metadataVersion > 0) %>% 
  group_by(publisher) %>% 
  summarize(count=n(), AvgNversions = round(mean(metadataVersion),2)) %>% 
  arrange(desc(count)) %>% 
  datatable()

How to collapse by version? Maybe that’s for another day…

DataCite Affiliation data

Look at repositories with affiliation and publication years prior to 2014

DataCite released affiliation as a metadata option on Oct 16. 2014. The repositories with affiliations for things published before then may have been back-updated?

What repositories have publications with affiliation before then?

all_dois_collapsed %>% 
  group_by(publisher, publicationYear) %>% 
  summarize(count=n()) %>% 
  arrange(publicationYear) %>% 
  pivot_wider(names_from = publicationYear, 
              values_from = count) %>% 
  arrange(2012, 2013, 2014, 2015) %>% 
  datatable()

Completeness

Looking at fields that are recommended by OSTP and DataCite:

• Resource Author (creators)
• Resource Publication Date (publication year)
• Funder Project Identifier (fundingReferences)
• Project Funder (fundingReferences)
• Resource Author Affiliation (creators)
• Funder Identifier (fundingReferences)
• Date Created (dates)
• Resource Publisher (publisher)
• Rights (rightsList)
• Abstract (descriptions)
• Keyword (subjects)
• Resource Author Affiliation Identifier (creators)
• Resource Author Identifier (creators)
• Related Identifiers (relatedIdentifiers)

Subset the data to these fields

for_metadata <- all_dois_collapsed %>% 
  select(institution, group, DOI, creators, publicationYear, fundingReferences, dates, publisher, rightsList, descriptions, subjects, relatedIdentifiers) %>% 
  mutate(RowID = 1:nrow(.))

#create function to return if at least one is not NA or NULL
atleastonevalid <- function(x) {
  sum(!is.na(x)) > 0 &
    sum(x != "") > 0 &
    sum(x != "NULL") > 0}

Creator fields (author name, affiliation, name identifiers, affiliation identifier)

#each are nested in an item within a list, so need to unnest 
for_metadata$creators1 <- lapply(for_metadata$creators, function(x) x[[1]])

creators <- for_metadata %>% 
  select(RowID, publisher, creators1) %>% 
  unnest(cols = creators1, keep_empty = T) 

#make lists in nameIdentifiers an empty dataframe


#make each column of interest a vector
creators$affiliation1 <- lapply(creators$affiliation, paste, collapse=",")
creators$nameIdentifier <- lapply(creators$nameIdentifiers, paste, collapse=",")

creator_table <- creators %>% 
  group_by(RowID) %>% 
  summarize(has_name = atleastonevalid(name), 
            has_affiliation = atleastonevalid(affiliation1), 
            has_nameIdentifier = atleastonevalid(nameIdentifier),
            count=n())

#Some quick accuracy checks
noname <- filter(for_metadata, RowID %in% creator_table$RowID[which(creator_table$has_name==FALSE)])
#seem to be the crossref ones

Publication Year and dates

for_metadata$dates1 <-lapply(for_metadata$dates, function(x) x[[1]])

dates <- for_metadata %>% 
  unnest(dates1, keep_empty = T)

date_table <- dates %>% 
  pivot_wider(names_from = dateType, 
              values_from = date, 
              names_prefix = "date_") %>% 
  group_by(RowID) %>% 
  summarize(has_pubYear = atleastonevalid(publicationYear), 
            has_dateCreated = atleastonevalid(date_Created), 
            has_dateIssued = atleastonevalid(date_Issued), 
            has_dateCollected = atleastonevalid(date_Collected))

Funder Information

for_metadata$fundingReferences1 <- lapply(for_metadata$fundingReferences, function(x) x[[1]])

#pull out the unique funder variables in the data
fundervariables <- unique(unlist((lapply(for_metadata$fundingReferences1, function(x) names(x)))))

#add to dataset
for_metadata <- as.data.frame(for_metadata)

#fill in with whether that variable is present in each row of metadata
containsfundervar <- lapply(for_metadata$fundingReferences1, function(x) fundervariables %in% names(x))
names(containsfundervar) <- for_metadata$RowID

for_metadata[,fundervariables] <- do.call(rbind, containsfundervar)

funding_table <- for_metadata %>% 
  select(RowID, all_of(fundervariables))

Rights

for_metadata$rightsList1 <- lapply(for_metadata$rightsList, function(x) x[[1]])

rightsvariables <- unique(unlist((lapply(for_metadata$rightsList1, function(x) names(x)))))

#fill in with whether that variable is present in each row of metadata
containsrightsvar <- lapply(for_metadata$rightsList1, function(x) rightsvariables %in% names(x))
names(containsrightsvar) <- for_metadata$RowID

for_metadata[,rightsvariables] <- do.call(rbind, containsrightsvar)

rights_table <- for_metadata %>% 
  select(RowID, all_of(rightsvariables))

Descriptions (includes abstract)

for_metadata$descriptions1 <-lapply(for_metadata$descriptions, function(x) x[[1]])

descvariables <- unique(unlist((lapply(for_metadata$descriptions1, function(x) names(x)))))

#fill in with whether that variable is present in each row of metadata
containsdescvar <- lapply(for_metadata$descriptions1, function(x) descvariables %in% names(x))
names(containsdescvar) <- for_metadata$RowID

for_metadata[,descvariables] <- do.call(rbind, containsdescvar)

desc_table <- for_metadata %>% 
  select(RowID, all_of(descvariables))

Related identifiers

for_metadata$relatedIdentifiers <- lapply(for_metadata$relatedIdentifiers, function(x) x[[1]])

idvariables <- unique(unlist((lapply(for_metadata$relatedIdentifiers, function(x) names(x)))))

#fill in with whether that variable is present in each row of metadata
containsidvar <- lapply(for_metadata$relatedIdentifiers, function(x) idvariables %in% names(x))
names(containsidvar) <- for_metadata$RowID

for_metadata[,idvariables] <- do.call(rbind, containsidvar)

relid_table <- for_metadata %>% 
  select(RowID, all_of(idvariables))

Subjects

for_metadata$subjects <- lapply(for_metadata$subjects, function(x) x[[1]])

subvariables <- unique(unlist((lapply(for_metadata$subjects, function(x) names(x)))))

#fill in with whether that variable is present in each row of metadata
containssubvar <- lapply(for_metadata$subjects, function(x) subvariables %in% names(x))
names(containssubvar) <- for_metadata$RowID

for_metadata[,subvariables] <- do.call(rbind, containssubvar)

sub_table <- for_metadata %>% 
  select(RowID, all_of(subvariables))

Combine all of them and select relevant fields

all_dois_collapsed_completeness <- for_metadata %>% 
  select(RowID, DOI,publisher, institution, group) %>% 
  full_join(creator_table, by="RowID") %>% 
  full_join(date_table, by="RowID") %>% 
  full_join(funding_table, by="RowID") %>% 
  full_join(desc_table, by="RowID") %>% 
  full_join(relid_table, by="RowID") %>% 
  full_join(rights_table, by="RowID") %>% 
  full_join(sub_table, by="RowID") %>% 
  select(RowID,DOI, publisher, institution,group, has_name, has_affiliation, has_nameIdentifier, has_pubYear, has_dateCreated, funderName, awardNumber, funderIdentifier, description, relatedIdentifier, rights, subject, lang)

Then create dataset with indicators for whether fields have information in them (only indicates presence of information, not quality of information).

all_dois_collapsed_completenessl <- all_dois_collapsed_completeness %>% 
  pivot_longer(cols=has_name:lang, 
               names_to = "variable", 
               values_to = "value") %>% 
  mutate(variable = gsub("has_", "", variable))

Table of metadata completeness for 10 sample DOIs from each publisher

all_dois_collapsed_completeness %>% 
  filter(publisher %in% top10pubs) %>% 
   filter(group != "Affiliation - CrossRef") %>% 
  select(-RowID, -institution, -group) %>% 
  group_by(publisher) %>% 
  slice_head(n=10) %>% 
  datatable(options = list(
  pageLength = 20, scrollX = TRUE))

Completeness of Top DataCite Repositories

by_publisher_complete_dc <- all_dois_collapsed_completenessl %>% 
  filter(publisher %in% top10pubs) %>% 
  filter(group != "Affiliation - CrossRef") %>% 
  group_by(publisher, variable) %>% 
  summarize(complete = sum(value), total = n()) %>% 
  mutate(percent_complete = complete/total*100)

#organize the variables by completeness
compvarorder <- by_publisher_complete_dc %>% 
  group_by(variable) %>% 
  summarize(avgcomp = mean(percent_complete, na.rm=T)) %>% 
  arrange(desc(avgcomp))

(completepub <- by_publisher_complete_dc %>% 
  mutate(variable = factor(variable, levels = compvarorder$variable)) %>% 
  ggplot(aes(x=variable, y=percent_complete, group=publisher)) +
  geom_line(aes(color=publisher), position = position_jitter(height = 1, width = .1), linewidth = 1) +
  scale_color_manual(values = top10colors, name="Publisher") +
  labs(x="DataCite Metadata Field", y = "Percent Records Complete") +
  theme_bw() + 
  guides(color = guide_legend(nrow = 2, title.position = "top")) +
  theme(legend.position = "bottom", legend.title.align = .5, 
        axis.text.x = element_text(angle=90, hjust = 1, vjust = .5)))

ggsave(plot = completepub, filename = "figures/CompletenessData_allDatacite.png", width = 10, height = 5.25, units="in")

Completeness of Individual Top Non-IR Repositories

Dryad

Figshare

Harvard Dataverse

ICPSR

Zenodo

Completeness of IR Repositories

by_publisher_complete_ir <- all_dois_collapsed_completenessl %>% 
 filter(publisher == "Institutional Repository") %>% 
  filter(institution != "Duke") %>% 
  group_by(institution, variable) %>% 
  summarize(complete = sum(value, na.rm = T), total = n()) %>% 
  mutate(percent_complete = complete/total*100)

#organize the variables by completeness
compvarorderIR <- by_publisher_complete_ir %>% 
  group_by(variable) %>% 
  summarize(avgcomp = mean(percent_complete, na.rm=T)) %>% 
  arrange(desc(avgcomp))

instcolors <- c("Cornell" = "#B31B1B", 
                "Duke" = "#00539B", 
                "Michigan" = "#FFCB05", # #00274C
                "Minnesota" = "#7a0019", 
                "Virginia Tech" = "#E87722", 
                "Washington U" = "#6c7373")

Combined plot for IRs

(completeIRpub <- by_publisher_complete_ir %>% 
  mutate(variable = factor(variable, levels = compvarorderIR$variable)) %>% 
  ggplot(aes(x=variable, y=percent_complete, group=institution)) +
  geom_line(aes(color=institution), position = position_jitter(height = 1, width = .1), linewidth = 1) +
  scale_color_manual(values = instcolors , name="Institutional Repository") +
  labs(x="DataCite Metadata Field", y = "Percent Records Complete") +
  theme_bw() + 
  guides(color = guide_legend(nrow = 2, title.position = "top")) +
  theme(legend.position = "bottom", legend.title.align = .5, 
        axis.text.x = element_text(angle=90, hjust = 1, vjust = .5)))

ggsave(plot = completepub, filename = "figures/CompletenessData_IRDatacite.png", width = 10, height = 5.25, units="in")

Cornell

Duke

NOTE: Duke metadata came from CrossRef so this plot is removed

Michigan

Minnesota

Virginia Tech

Wash U

Write out institutional data

Write out CSV files for each institution:

• All DOIs
• All DOIs collapsed

for (i in unique(all_dois$institution)) {
  all_dois %>% 
    filter(institution == i) %>% 
    write.csv(file=paste0("data_all_dois/All_dois_", i, gsub("-", "", Sys.Date()), ".csv"), row.names = F)
  
  all_dois_collapsed %>% 
    filter(institution == i) %>% 
    write.csv(file=paste0("data_all_dois/All_dois_collapsed_", i, gsub("-", "", Sys.Date()), ".csv"), row.names = F)
}